refactor: Separate Each Class into Each Module

Statistic_Calculation.py → Calculations.py

@@ -1,5 +1,4 @@
-# Statistical Calculations Class
-class Statistic_Calculation:
+class Describe:
     @staticmethod
     def mean(dataset):
         return sum(dataset) / len(dataset)
@@ -22,14 +21,14 @@ def kurtosis(dataset, mean, deviation):
         n = len(dataset)
         return (n * (n + 1) * sum(((x - mean) / deviation) ** 4 for x in dataset) /
                 ((n - 1) * (n - 2) * (n - 3))) - (3 * (n - 1) ** 2) / ((n - 2) * (n - 3))
-    
+
     @staticmethod
     def median(dataset):
         sorted_data = sorted(dataset)
         n = len(sorted_data)
         mid = n // 2
 
-        if n % 2 == 0:  # If even, return the average of the two middle numbers
+        if n % 2 == 0:
             return (sorted_data[mid - 1] + sorted_data[mid]) / 2
-        else:  # If odd, return the middle number
+        else:
             return sorted_data[mid]

FrequencyTable.py

@@ -1,85 +1,31 @@
 import numpy as np
+from Calculations import Describe
+from Summary import Result
 
-class StatisticalCalculations:
-    @staticmethod
-    def mean(dataset):
-        return sum(dataset) / len(dataset)
-
-    @staticmethod
-    def variance(dataset, mean):
-        return sum((x - mean) ** 2 for x in dataset) / len(dataset)
-
-    @staticmethod
-    def standard_deviation(variance):
-        return variance ** 0.5
-
-    @staticmethod
-    def skewness(dataset, mean, deviation):
-        n = len(dataset)
-        return (n / ((n - 1) * (n - 2))) * sum(((x - mean) / deviation) ** 3 for x in dataset)
-
-    @staticmethod
-    def kurtosis(dataset, mean, deviation):
-        n = len(dataset)
-        return (n * (n + 1) * sum(((x - mean) / deviation) ** 4 for x in dataset) /
-                ((n - 1) * (n - 2) * (n - 3))) - (3 * (n - 1) ** 2) / ((n - 2) * (n - 3))
-
-    @staticmethod
-    def median(dataset):
-        sorted_data = sorted(dataset)
-        n = len(sorted_data)
-        mid = n // 2
-
-        if n % 2 == 0:  # If even, return the average of the two middle numbers
-            return (sorted_data[mid - 1] + sorted_data[mid]) / 2
-        else:  # If odd, return the middle number
-            return sorted_data[mid]
-
-# Processed Data Assignment 
-class ProcessedData:
-    def __init__(self, data, bot, top, bot_L, top_L, F, R, L, M, bot_CF, top_CF, RF, mode):
-        self.top = top
-        self.limit = L     
-        self.ranges = R      
-        self.bottom = bot
-        self.midpoint = M
-        self.frequency = F
-        self.classval = data
-        self.top_limit = top_L
-        self.bottom_limit = bot_L
-        self.mode = mode
-        self.bottom_cumulative_frequency = bot_CF
-        self.top_cumulative_frequency = top_CF
-        self.relative_frequency = RF
-        self.percentage_relative_frequency = [f"{rf:.2f}%" for rf in self.relative_frequency]
-
-# Frequency Table Class 
-class FrequencyTable:
+class Classify:
     def __init__(self, dataset):
-        # Check for mixed data types
         if any(isinstance(item, str) for item in dataset) and any(isinstance(item, (int, float)) for item in dataset):
             raise ValueError("Data is corrupted: contains both numeric and string values.")
 
-        # Data Initiation
         self.dataset = sorted(dataset)
         self.length = len(dataset)
         self.lowest = min(dataset) if isinstance(dataset[0], (int, float)) else None
         self.highest = max(dataset) if isinstance(dataset[0], (int, float)) else None
 
-        if self.lowest is not None:  # Only calculate classes for numeric data
-            self.calculate_statistics()
-            self.calculate_classes()
+        if self.lowest is not None:
+            self._calculate_statistics()
+            self._calculate_classes()
 
-    def calculate_statistics(self):
+    def _calculate_statistics(self):
         self.sum = sum(self.dataset)
-        self.mean = StatisticalCalculations.mean(self.dataset)
-        self.median = StatisticalCalculations.median(self.dataset)
-        self.variance = StatisticalCalculations.variance(self.dataset, self.mean)
-        self.deviation = StatisticalCalculations.standard_deviation(self.variance)
-        self.skewness = StatisticalCalculations.skewness(self.dataset, self.mean, self.deviation)
-        self.kurtosis = StatisticalCalculations.kurtosis(self.dataset, self.mean, self.deviation)
-
-    def calculate_classes(self):
+        self.mean = Describe.mean(self.dataset)
+        self.median = Describe.median(self.dataset)
+        self.variance = Describe.variance(self.dataset, self.mean)
+        self.deviation = Describe.standard_deviation(self.variance)
+        self.skewness = Describe.skewness(self.dataset, self.mean, self.deviation)
+        self.kurtosis = Describe.kurtosis(self.dataset, self.mean, self.deviation)
+
+    def _calculate_classes(self):
         self.classes = 1 + (3.222 * np.log10(self.length))
         self.classes = round(self.classes - 0.5)
         self.range = self.highest - self.lowest
@@ -113,8 +59,9 @@ def populate_grouped(self):
 
         # Initiating Variables for Frequency Table
         current_number = self.base - 1
+        top_cumulative_freq = 1
 
-        while True:
+        while top_cumulative_freq != 0:
             old_number = current_number + 1
             self.bottom.append(old_number)
 
@@ -144,15 +91,12 @@ def populate_grouped(self):
             current_relative_frequency = np.round((current_frequency / self.length) * 100)
             self.relative_frequency.append(current_relative_frequency)
 
-            if current_frequency == 0:
-                break
-
         # Find Mode
         mode_index = [i for i, val in enumerate(self.frequency) if val == max(self.frequency)]
         self.mode = [self.data_range[i] for i in mode_index]
 
-        # Create ProcessedData object
-        self.grouped = ProcessedData(None, self.bottom, self.top, self.bottom_limit, self.top_limit,
+        # Create Result object
+        self.grouped = Result(None, self.bottom, self.top, self.bottom_limit, self.top_limit,
                                      self.frequency, self.data_range, self.data_limit, self.data_midpoint,
                                      self.bot_cumulative_frequency, self.top_cumulative_frequency,
                                      self.relative_frequency, self.mode,
@@ -184,8 +128,8 @@ def populate_simple(self):
         mode_index = [i for i, val in enumerate(self.frequency) if val == max(self.frequency)]
         self.mode = [unique_data[i] for i in mode_index]
 
-        # Create ProcessedData object
-        self.simple = ProcessedData(
+        # Create Result object
+        self.simple = Result(
             unique_data, None, None, self.bottom_limit, self.top_limit, 
             self.frequency, None, None, None, 
             self.bot_cumulative_frequency, self.top_cumulative_frequency, 

Main.py

@@ -1,5 +1,5 @@
 # EXAMPLE PROGRAM
-import FrequencyTable as ft
+from FrequencyTable import Classify
 import tabulate as tabulate
 import Transform as tf
 import pandas as pd
@@ -18,9 +18,8 @@
     12.9, 12.8, 12.7, 12.6, 12.5, 12.4
 ]
 
-
 # Initiate Object From The Raw Data
-data = ft.FrequencyTable(dataset)
+data = Classify(dataset)
 data.populate_simple() # Simple Data
 
 # Simple Populated Data
@@ -67,6 +66,7 @@
     headers='keys',
     tablefmt='pipe',
 )
+print(data.interval)
 print(tablegrouped)
 print(data.grouped.mode)
 print(data.mean)

README.md

@@ -2,26 +2,24 @@
 > My Statistic Tools made with Python 
 
 ### Features
-- Frequency Table
-- Descriptive Statistics
-- Display Chart
-- Data Transformation
+- Auto Classify Data Into Frequency Table
+- Descriptive Statistics Calculation
+- Many Data Transformation Calculation
 
 ### Required
-- Matplotlib
-- Matplotlib_Venn
 - Numpy
-- Tabulate
-- Pandas
 - Scipy
+- Tabulate  ( Optional for printing Frequency Table )
+- Pandas    ( Optional for printing Frequency Table )
 
 ### Structure
 - Program Structure
 
       + Main.py                [ Main Control file ]
       + FrequencyTable.py      [ Frequency Table Module ]
-      + Chart.py               [ Chart Display Module ]
       + Transform.py           [ Data Transformation Module ]
+      + Calculations.py        [ Descriptive Statistic Module ]
+      + Summary.py             [ Processed Data Append in Here ]
 
 ### How to Use
 - For detailed Documentation, please visit [Pythistic Wiki](https://github.com/brotherzhafif/Pythistic/wiki)

Summary.py

@@ -0,0 +1,16 @@
+class Result:
+    def __init__(self, data, bot, top, bot_L, top_L, F, R, L, M, bot_CF, top_CF, RF, mode):
+        self.top = top
+        self.limit = L
+        self.ranges = R
+        self.bottom = bot
+        self.midpoint = M
+        self.frequency = F
+        self.classval = data
+        self.top_limit = top_L
+        self.bottom_limit = bot_L
+        self.mode = mode
+        self.bottom_cumulative_frequency = bot_CF
+        self.top_cumulative_frequency = top_CF
+        self.relative_frequency = RF
+        self.percentage_relative_frequency = [f"{rf:.2f}%" for rf in self.relative_frequency]